Abstract. For forecasting the maximum 5-day accumulated precipitation
over the winter season at lead times of 3, 6, 9 and 12 months
over Canada from 1950 to 2007, two nonlinear and two linear
regression models were used, where the models were support
vector regression (SVR) (nonlinear and linear versions),
nonlinear Bayesian neural network (BNN) and multiple linear
regression (MLR). The 118 stations were grouped into six
geographic regions by K-means clustering. For each region,
the leading principal components of the winter maximum 5-d
accumulated precipitation anomalies were the predictands. Potential predictors
included quasi-global sea surface temperature anomalies and
500 hPa geopotential height anomalies over the Northern
Hemisphere, as well as six climate indices (the Niño-3.4
region sea surface temperature, the North Atlantic
Oscillation, the Pacific-North American teleconnection, the
Pacific Decadal Oscillation, the Scandinavia pattern, and the
East Atlantic pattern). The results showed that in general the
two robust SVR models tended to have better forecast skills
than the two non-robust models (MLR and BNN), and the
nonlinear SVR model tended to forecast slightly better than
the linear SVR model. Among the six regions, the
Prairies region displayed the highest forecast skills, and the
Arctic region the second highest. The strongest nonlinearity
was manifested over the Prairies and the weakest
nonlinearity over the Arctic.